What is the rationale behind the Climate for Sectors Dashboard?

Climate variability and change impose different problems for different applications. While general trends can be used to outline emerging challenges, the dominant factors affecting ecosystems and people vary strongly on a case-by-case basis. Challenges often arise from quite specific characteristics of the overall climate signal and its changes. For instance, transportation and energy sectors are vulnerable to the highest daily temperatures, while agricultural production is sensitive to seasonal temperature increases or at parts of the growth cycle. Human or livestock health might be most sensitive to a combination of temperature changes together with high moisture patterns. The same applies to rainfall or water availability. A change in the total rainfall might be important for reservoir operators. However, rain-fed agriculture requires a more specific information on rainfall patterns during specific times of the agricultural cycles. Therefore, it is critical to assemble collections of climate indicators for different sectors, which are useful for understanding climate variability and impacts and facilitating decision-making process on the ground.

To address such needs, CCKP has designed and implemented a Climate for Sectors Dashboard for Agriculture, Water, Energy, and Health sectors. Each dashboard collect information from multiple climate indicators and present them with simple, embedded interpretation for an informative, high-level summary of the potential for future climate change impacts. The selection and visualization of key climate indicators per sector was conducted through a participatory consultation process with sector specialists and development project leaders.

Need further information?

In addition to the selected key sectoral climate variables, over 38 new climate indicators are available at the global level within the CCKP climate datasets. All datasets are available for download. Detailed metadata can be found here.

If you have any questions, please feel free to contact us at climateportal@worldbank.org.

Introduction
Obviously, the supply of water is directly affected by weather and climate. Next to the critical water input through precipitation at daily, monthly and seasonal scales, also the loss through evapotranspiration should be taken into consideration. Particularly high temperatures, low humidity and high winds can efficiently remove water from the land surface. Equally, the demand for water is expected to evolve under climate change, particularly as they relate to often rapidly changing demographic and economic settings. These changes generally increase the operational challenges and risk for the water sector. (Note: you can access the full list of climate indicators here).

Precipitation:

Seasonal Variability
Change in Mean Monthly Precipitaion
Data
The graph shows projected change in Monthly Mean Precipitation per month by 2050 compared to the reference period (1986-2005) under all RCPs of CIMP5 ensemble modeling. Positive values indicate that monthly rainfall will likely to increase compared to the baseline, and vice versa. The shaded area represents the range between the 10th and 90th percentile of all climate projections.

Implications
The annual distribution of rainfall is of great interest to the water industry..


Caveats
The median is a fairly robust measure as it reduces the lower quality simulations..


Precipitation:

Time Series
Number of Days with Very Heavy Precipitation
Data
The graph shows the recorded number of Days with Vey Heavy Rainfall (20mm/day) each year for 1986-2005, and projected values for 2020-2100 under all RCPs of CIMP5 ensemble modeling. Note, the shaded ranges illustrate the inter-model differences, here using the +/- one standard deviation. The reason for using a narrower metric compared to the 10th and 90th percentile is that the inter-model difference is large for precipitation, and in particular for the count of days with rainfall.

Implications
Raising temperatures bring along a change in the potential carrying capacity of moisture..


Caveats
The representation of intense rainfall is a challenge for coarse resolution climate ..


Precipitation:

Extreme Events
5-Day Precipitaion: 25-yr Return Level
Data
The boxplot shows recorded 5-Day Cumulative Rainfall for 1986-2005 and projected 5-Day Cumulative Rainfall 25-yr Return Level by 2050 under all RCPs of CIMP5 ensemble modeling. This indicator focuses on the maximum 5-day cumulative rainfall amount that can be expected within a 25-yr period.

Implications
The most extreme rainfall episodes generally have the danger of leading to significant..


Caveats
The calculation of return levels, the maximum amount expected over a given time..


Drought:

Spatial Variability
Change in Annual Likelihood of Severe Drought
Data
The map shows change in projected Annual Likelihood of Severe Drought by 2050 compared to the reference period (1986-2005) under RCP 8.5 of CIMP5 ensemble modeling. Brown/Yellow areas are more likely to experience severe drought compared to the baseline period. Meanwhile, Blue/Green areas are less likely to experience severe drought.

Implications
Changes in the water balance is of increasing concern on a warmer planet..


Caveats
Drought projections are somewhat controversial because a large part of the..


Introduction
The exposure of the irrigation, crops and land management, livestock, rural transport, and storage and processing to climate variability and change increases at the local and regional scale. Climate change is enhancing the risks, acting as a threat multiplier, particularly with regard to the availability of water and the changes in thermal environment. In many places, climate change is expressing itself through higher variations in moisture, increase in dryness when dry, and increase in wetness when wet. (Note: you can access the full list of climate indicators here).

Temperature:

Seasonal Variability
Change in Daily Maximum Temperature
Data
The graph shows projected change in Monthly Mean of Daily Maximum Temperature by 2050 compared to the reference period (1986-2005) under all RCPs of CIMP5 ensemble modeling. Positive values indicate that warmest daily maximum temperatures will likely to increase compared to the baseline, and vice versa. The shaded area represents the range or spread between 10th and 90th percentile of all analyzed models.

Implications
The warm conditions of the day are important for crop growth cycles. However, there..


Caveats
Across the collection of climate models, the overall temperature trends, independent of..


Precipitation:

Spatial Variability
Change in Annual Precipitaion
Data
The map shows change in projected Mean Annual Precipitation by 2050 compared to the reference period (1986-2005) under RCP 8.5 of CIMP5 ensemble modeling. Blue/Green areas are likely to receive more annual rainfall compared to the reference period and to Brown/Yellow areas.

Implications
Annual precipitation is one of the most fundamental climatic conditions for rain-fed..


Caveats
Global climate models have substantially improved their spatial representation of..


Precipitation:

Time Series
Maximum Number of Consecutive Dry Days
Data
The graph shows the recorded maximum number of Consecutive Dry Days (CDD) per year for 1986-2005, and projected maximum number of CDD for 2020-2100 under all RCPs of CIMP5 ensemble modeling. Note, the shaded ranges (or model spread) illustrate the inter-model differences, here using the +/- one standard deviation. The reason for using a narrower metric compared to the 10th and 90th percentile is that the inter-model difference is large for precipitation, and in particular for the count of days with rainfall.

Implications
A "dry day" is a day without any agriculturally meaningful rainfall, which is ..


Caveats
Temporal variations in the projections of the maximum length of consecutive dry ..


Precipitation:

Extreme Events
Monthly Precipitation: 10-yr Return Level
Data
The boxplot shows recorded Maximum Monthly Rainfall for 1986-2005 and projected Maximum Monthly Rainfall 10-yr Return Level by 2050 under all RCPs of CIMP5 ensemble modeling. This indicator focuses on the maximum monthly rainfall amount that can be expected within a 10-yr period.

Implications
An anticipated impact of climate change is the increase in climate variability..


Caveats
The focus of the analysis is on the largest accumulated monthly rainfall to..


Introduction
The energy sector is linked to climate variability and change in numerous ways. On one side, global energy production is a strong contributor to the drivers of climate change, namely through the emission of greenhouse gases. On the other side, it is also exposed to the diverse impacts of climate variability and change through changes in energy supply (e.g. disruption of operations and distribution) and demand (growing populations and evolving power needs). The consequences can be complex, yet they are often both positive and negative. (Note: you can access the full list of climate indicators here).

Temperature:

Spatial Variability
Annual Mean Temperature Change
Data
The map shows change in projected Annual Mean Temperature by 2050 compared to the reference period (1986-2005) under RCP 8.5 of CIMP5 ensemble modeling. Purple/Red areas are likely to experience annual temperature increase compared to baseline period. Meanwhile, Blue/Green areas are likely to experience annual temperature decrease.

Implications
Annual temperature provides the broadest assessment of the climate..


Caveats
Global climate models are quite well suited to project temperature changes..


Temperature:

Seasonal Variability
Change in Cooling Degree Days
Data
The graph shows projected change in Cooling Degree Days per month by 2050 compared to the reference period (1986-2005) under all RCPs of CIMP5 ensemble modeling. Positive values indicate that cooling degree days will likely to increase compared to the baseline, and vice versa. The shaded area represents the range between the 10th and 90th percentile of the model projections.

Implications
The relationship of daily heat with the demand for electricity can be estimated..


Caveats
The actual threshold chosen is less important than the changes. In most locations,..


Precipitation:

Extreme Events
5-Day Precipitaion: 10 yr Return Level
Data
The boxplot shows recorded 5-Day Cumulative Rainfall for 1986-2005 and projected 5-Day Cumulative Rainfall 10-yr Return Level by 2050 under all RCPs of CIMP5 ensemble modeling. This indicator focuses on the maximum 5-day cumulative rainfall amount that can be expected within a 10-yr period.

Implications
As warmer air has a higher capacity to carry moisture in form of water..


Caveats
Extremes are often behaving differently than the mean of a process. In many..


Drought:

Time Series
Drought / Wet Conditions (SPEI)
Data
The graph shows the recorded Mean Drought Index (or Standardized Precipitation Evapotranspiration Index, SPEI) per year for 1986-2005, and projected SPEI for 2020-2100 under all RCPs of CIMP5 ensemble modeling. Note, the shaded ranges illustrate the inter-model differences, here using the +/- one standard deviation. The reason for using a narrower metric compared to the 10th and 90th percentile is that the inter-model difference is large for precipitation, and in particular for the count of days with rainfall.

Implications
The direction of SPEI changes provide insight into increasing or decreasing pressure on..



Introduction
The human health sector has clear links to climate variability through both direct exposure as well as indirect pathways. Obviously, negative health impacts come from extreme climate events, such as heat waves, hurricanes/storms, floods and droughts. Gradual changes of climate affecting water, food and air quality also have negative influence on human health around the world. Beyond the physical effects are issues related to mental health. Research has shown that increased numbers of extreme events can leave significant fractions of the population with PTSD-like symptoms. Although controversial, studies indicate that there is linkage between rising temperatures and increase in aggression and violence in society. (Note: you can access the full list of climate indicators here).

Temperature:

Spatial Variability
Record High Temperature
Data
The map shows projected change in Maxima of Daily Maximum Temperature by 2050 compared to the reference period (1986-2005) under RCP 8.5 of CIMP5 ensemble modeling.

Implications
Climate change impacts on human health can come in many..


Temperature:

Seasonal Variability
Change in Number of Hheat Days
Data
The graph shows projected change in Number of Heat Days (Tmax > 35°C) per month by 2050 compared to the reference period (1986-2005) under all RCPs of CIMP5 ensemble modeling. Positive values indicate that number of heat days will likely to increase compared to the baseline, and vice versa. The shaded area represents the range between 10th and 90th percentile of the projection.

Implications
The annual distribution of days with a high heat index provides insight into..


Caveats
The calculation of the heat index requires both temperature and moisture output..


Temperature:

Time Series
Tropical Nights(>20�C)
Data
The graph shows the recorded number of Tropical Nights (Tmin > 20°C) per year for 1986-2005, and projected values for 2020-2100 under all RCPs of CIMP5 ensemble modeling. Note, the shaded ranges illustrate the inter-model differences, here using the +/- one standard deviation. The reason for using a narrower metric compared to the 10th and 90th percentile is that the inter-model difference is large for precipitation, and in particular for the count of days with rainfall.

Implications
The counterpart to daily peak temperatures is the nighttime cooling. Many organisms..


Caveats
As with other temperature fields, the confidence in the projection of changes in..


Temperature:

Extreme Events
Warmspell Duration Index
Data
The boxplot shows recorded Warm Spell Duration Index (WSDI) for 1986-2005 and projected WSDI by 2050 under all RCPs of CIMP5 ensemble modeling. The WSDI is a measure of such an uninterrupted sequence of at least 6 days that surpass the currently observed 95th percentile of temperature. It therefore not only reflects increases in temperature but looks at the likelihood of sequences of conditions that today are considered the warmest conditions in the year.

Implications
The hottest places in low latitudes don't necessarily have longer warm ..


Caveats
The median change for RCP8.5 is almost uniformly surpassing a doubling by 2050..